This thesis investigates the potential of recent machine learning methods for the challenging task of information extraction from single-channel audio where the source of interest is mixed with multiple interfering sources. World-leading results are demonstrated on challenging speech separation and recognition problems where speech is mixed with non-stationary background noise such as music. Furthermore, state-of-the-art results are presented in selected music information retrieval applications involving polyphonic audio. «

This thesis investigates the potential of recent machine learning methods for the challenging task of information extraction from single-channel audio where the source of interest is mixed with multiple interfering sources. World-leading results are demonstrated on challenging speech separation and recognition problems where speech is mixed with non-stationary background noise such as music. Furthermore, state-of-the-art results are presented in selected music information retrieval applications... »